Modelling commitment and compensation: a case study of a 52-year-old masters athlete
Bibliographic record
Abstract
Masters Athletes (MAs) are a highly unique cohort who participate in competitive sport in adulthood. Understanding the factors facilitating one athlete’s personal commitment may illustrate the nature of adaptive strategies for remaining active in sport. In this case study, Andrew (pseudonym), a 52- year-old nationally ranked Canadian runner, provincially ranked squash player and regional cross-country skiing champion was interviewed about personal and social conditions facilitating his sport commitment and strategies he used to maintain elite performance. Andrew’s accounts were deductively analysed using the Sport Commitment Model(SCM) and the Model of Selective Optimisation with Compensation (MSOC). With respect to the SCM, Andrew committed to sport because he inherently enjoyed training and competing, benefited from social connections around sport, and was afforded opportunities to compete, travel to new places and feel youthful. With respect to the MSOC, Andrew sustained year-round activity by prioritising his sports and reducing his participation intensity in low-ranked activities before major competitions in other activities. Moreover, he increased his sport-specific practice, and used his knowledge and experience to alter his techniques and training to compensate for age-related losses. Results supported the aforementioned models for understanding why MAs remain committed and how they remain proficient in sport.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".